Optimum use of MACE information to predict composite type traits
Bibliographic record
Abstract
Since August 1999, Interbull evaluations for conformation traits have been available for the Holstein breed and for other breeds in subsequent years. The current routine includes 16 linear traits and three composite traits (overall conformation, overall udder and overall feet & legs). Not all countries score the 16 ‘Interbull’ linear traits and some countries score a higher number of type traits in their classification program. For example, the Canadian classification system scores 22 linear traits and five major scorecard traits (conformation, mammary system, feet and legs, rump and dairy strength), plus a series of defective characteristics. Countries differ also in the way they collect overall conformation. Some countries calculate overall conformation from the EBV of the linear traits, while others score overall conformation and estimate breeding values directly. As a consequence, countries also differ on how they publish MACE EBV for foreign bulls for overall conformation. Most countries predict the MACE EBV for conformation using the MACE EBV for the available linear traits, while others publish directly the EBV for overall conformation originating from MACE. Some European countries have challenged the latter practice, saying that their bulls were penalized (on those foreign scales where the MACE EBV was published directly) by this system. However, each country is responsible for the publication procedure of foreign bulls in their own scale. Thus, in the last few years some Interbull member countries have started to compute a second overall conformation with the objective of maximizing the genetic correlation with the United States. Since 2004, Canada has used a blending approach for MACE composite traits, which optimizes the use of all information from Interbull evaluations (Miglior et al., 2004). The objective of this study was to illustrate the method when applied on country scales other than Canada and to compare it, in terms of average reliability, with using the MACE EBV or a Predicted EBV computed from linear traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".